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Key findings | Baseline medication reconciliation practice was poor at both sites. Post-general educational intervention, medication discrepancy was significantly reduced by 42.8% at the intervention site ( | PMC10652589 | ||
Conclusions | The educational interventions improved pharmacists’ medication reconciliation practice at the intervention site. It is expected that this research would help create awareness on medication reconciliation among pharmacists in developing countries, with a view to reducing medication-related patient harm. | PMC10652589 | ||
Keywords | PMC10652589 | |||
Background | ambulatory diabetes, hypertensive | EVENT | Medication error is defined as any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient, or consumer [Medication reconciliation, which is intended to minimize medication discrepancies and possible inciden... | PMC10652589 |
Methods | PMC10652589 | |||
Study design and setting | A mixed-method non-randomised clinical trial was carried out at two teaching healthcare facilities in Nigeria. The study was carried out at the University College Hospital, Ibadan (intervention site), a 950-bed teaching hospital affiliated with University of Ibadan. The University of Ilorin Teaching Hospital, Ilorin (c... | PMC10652589 | ||
Inclusion and exclusion criteria | hypertension, diabetes | HYPERTENSION, DIABETES | Pharmacists who gave their informed consent to participate in the study were recruited at both sites. Undergraduate pharmacy students on experiential rotation were excluded from the study. Patients (18 years and above) diagnosed with diabetes and/or hypertension who visited the Endocrinology or Cardiology Clinics were ... | PMC10652589 |
Data collection instruments | hypertensive, diabetes | DIABETES | Three semi-structured questionnaires (Q1, Q2 and Q3) were used as the data collection instrument. The questionnaires were developed by the authors based on their teaching and practise experience, and extensive literature review [The data collection instrument for patients was pretested for face validity among 34 diabet... | PMC10652589 |
Recruitment of participants and data collection | hypertensive, intervention-46, 140 diabetes, hypertension, diabetes | HYPERTENSION, DISEASES, DIABETES | Sequel to acquiring ethics approval from each hospital review board, the approvals of heads of different units/departments where the study was undertaken were also secured. Total sampling of the entire pharmacists at the control and intervention sites was adopted for the study. The purpose of the study was explained to... | PMC10652589 |
Educational interventions | BEST | Two educational interventions were carried out by the principal investigator during the study, who is a faculty and a doctorate student working on medication reconciliation at the Department of Clinical Pharmacy and Pharmacy Administration, Faculty of Pharmacy, University of Ibadan, Nigeria. He underwent a two-week tra... | PMC10652589 | |
Data analysis | comorbidity | Data was summarized with descriptive and inferential statistics using SPSS for Windows Version 23.0 (IBM Corp, New York, USA). Normal distribution of the data was evaluated using Kolmogorov-Smirnov test. Inferential statistics such as Fisher’s exact test was done to compare associations between absence/presence of medi... | PMC10652589 | |
Discussion | This study revealed poor baseline medication reconciliation practice among pharmacists at both study sites. The focused educational intervention especially improved the practice of medication reconciliation by pharmacists at the intervention site. There was a reduction in medication discrepancies and an increase in det... | PMC10652589 | ||
Study limitations | hypertensive, diabetes | DIABETES | The attrition rate observed among pharmacists at both sites was quite high. This level of attrition was because a few pharmacists were posted out of the study sites, some dropped out for personal reasons, while some were on leave at different times during the study period. Since the study was carried out among ambulato... | PMC10652589 |
Conclusion | The educational interventions improved intervention pharmacists’ medication reconciliation practice and led to prevention of medication-related harm to patients. It is recommended that this intervention be replicated in more hospitals in Nigeria to encourage implementation of best practices. | PMC10652589 | ||
Acknowledgements | Not applicable. | PMC10652589 | ||
Authors’ contributions | Akinniyi A., Oluwakemi A. | Dr. Akinniyi A. Aje: Principal investigator and corresponding author. Contributions: Study design, data collection and analysis, manuscript writing. Dr. Segun J. Showande: Independent assessment of medication reconciliation data form retrieved from the study participants at the Geriatric Center, data analysis, manuscri... | PMC10652589 | |
Funding | Not applicable. | PMC10652589 | ||
Availability of data and materials | The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. | PMC10652589 | ||
Declarations | PMC10652589 | |||
Ethics approval and consent to participate | Approval for the study was granted by University of Ilorin Teaching Hospital Ethics Research Committee (ERC/PAN/2018/08/1814) and the joint University of Ibadan/University College Hospital Health Research and Ethics Committee (UI/EC/15/0308). The study was registered on ClinicalTrials.gov (ID NCT03182972) on 09/06/2017... | PMC10652589 | ||
Consent for publication | Not applicable. | PMC10652589 | ||
Competing interests | The authors declare no competing interests. | PMC10652589 | ||
References | PMC10652589 | |||
Subject terms | Combined oral contraceptives (COC) are among the most commonly used contraceptive methods worldwide, and mood side effects are the major reason for discontinuation of treatment. We here investigate the directed connectivity patterns associated with the mood side effects of an androgenic COC in a double-blind randomized... | PMC10276024 | ||
Introduction | depressed mood, adverse mood side effects, depressive symptoms | Combined oral contraceptives (COC) are among the most commonly used contraceptive methods worldwide [Although mood-related side effects are usually more pronounced in women with a history of depressive symptoms, depressed mood is not consistently reported to change after treatment in RCTs [Recent years have witnessed a... | PMC10276024 | |
Materials and methods | PMC10276024 | |||
Experimental design | Participants were scanned twice, first during a pre-treatment cycle (day 4 ± 3 after onset of menses) and secondly, during the last week of the treatment cycle (day 15–21 after start of treatment). The participants started taking the pill on the first day of menses (Fig. | PMC10276024 | ||
Experimental design. | Each participant had two sessions, before and during treatment (day 1–10 and day 15–21 respectively after onset of menses). Therefore, for the placebo group endogenous hormone levels increased from the first to the second appointment, while in the COC group synthetic hormone levels were stable and endogenous hormones l... | PMC10276024 | ||
Data acquisition | BEST | Functional and structural images were acquired on a Philips Achieva 3.0 T scanner using an 8-channel head coil (Philips Medical Systems, Best, The Netherlands). For the 5 min resting state a single shot echo planar imaging sequence was used to collect 100 volumes of BOLD data with a voxel size of 3.0 × 3.0 × 3.0 mm | PMC10276024 | |
Preprocessing | Scanner DICOM images were first converted to NIfTI files with MRIcron ( | PMC10276024 | ||
Spectral dynamic causal modeling and parametric empirical bayes | Resting state functional images were modelled using a (Bayesian) hierarchical random effects framework and spectral DCM was specified and inverted using DCM12 as implemented in SPM12 (In order to compare changes in the treatment group to the changes observed in the placebo group, we ran a 3-level hierarchical analysis ... | PMC10276024 | ||
Cross-validation | SIDE EFFECT | In order to check whether the mood side effect related effective connectivity could predict the assignment of participants to one group or another (COC vs. placebo) we used a leave-one-out scheme (spm_dcm_loo.m) as described in [ | PMC10276024 | |
Changes only in the placebo group | From the follicular to the luteal phase, within-network connectivity increased in the DMN (from the right AG to the mPFC), and in the SN (from the left AI to the right AI). Within the ECN, we found a lateralized pattern: while connectivity increased from parietal to frontal ECN in the right hemisphere, connectivity dec... | PMC10276024 | ||
Changes only in the COC group | From pre- to during treatment MRI-session, within-network connectivity increased in the DMN (from the PCC to the right AG and viceversa), while it decreased in the ECN (from the right SMG to the left MFG). No significant changes were observed in the within-network connectivity of the SN.Regarding the between-networks c... | PMC10276024 | ||
Changes in opposite direction for COC and the placebo group | In the following, an increase refers to increased connectivity in the COC, but decreased connectivity in the placebo group; while a decrease refers to decreased connectivity in the COC, but increased connectivity in the placebo group.Regarding the within-networks changes, we observed an increased connectivity in the DM... | PMC10276024 | ||
Summary of findings | Taking into account all the above, in the COC group compared to the placebo group, the within-network connectivity increased during treatment in the DMN, whereas it decreased in the SN and ECN. Regarding the between-network connectivity, specifically from the dACC (SN) to medial nodes of DMN, effective connectivity was... | PMC10276024 | ||
Associations of effective connectivity mood side effects | When making the previous distinction among the connectivity changes, some consistent patterns could be distinguished in the relation to the side effects (see Table Among those mood lability-related connections, the following connectivity changes surpassed the threshold of Ep > 0.20: from dACC to PCC, from rMFG to dACC,... | PMC10276024 | ||
Prediction of treatment by mood-related effective connectivity | The leave-one out cross-validation based on those connections identified above as showing the highest effect size and relation to mood lability (dACC → PCC, rMFG → dACC, rAG → rAI, rAG → dACC, rAG → rSMG) showed a significant association between the actual and predicted group of | PMC10276024 | ||
Leave-one-out cross-validation analysis. | Left: scatter plot displaying the correlation between the actual treatment group in the left-out-subject’s design matrix and the predicted treatment group based on the left-out-subject’s connectivity. Centre: the resulting posterior probability for each treatment group for each subject. Right: Differential connectivity... | PMC10276024 | ||
Discussion | mood deterioration, fatigue, anxiety, mood disorders, depressed mood, depressive, depressive symptoms | The main goal of the current manuscript was to characterize the changes in directed connectivity during COC treatment related to concurrent mood symptoms. Our results showed how effective connectivity changes noted during COC treatment related to mood deterioration. Mood lability was the most prominent COC-induced symp... | PMC10276024 | |
Supplementary information | The online version contains supplementary material available at 10.1038/s41398-023-02470-x. | PMC10276024 | ||
Acknowledgements | This research was funded by the Swedish Research Council project K2008–54X-200642–01–3, the Swedish Council for Working Life and Social Research projects 2007–1955, and 2007–2116, the Family Planning Foundation, and an unrestricted research grant from Bayer AB. The European Research Council (ERC) Starting Grant 850953 ... | PMC10276024 | ||
Author contributions | JE, IS-P, and MG designed and performed the clinical trial and acquired the data. EH-L was responsible for data curation and analysis, interpreting the results, drafting and revising the manuscript. MG, BP, and IS-P supervised the analysis, contributed in the results’ interpretation, and revised the manuscript. Both MG... | PMC10276024 | ||
Competing interests | Over the past three years, I. Sundstrom-Poromaa has served occasionally on advisory boards or acted as invited speaker at scientific meetings for Bayer Health Care, Gedeon Richter, Peptonics, Shire/Takeda, and Sandoz. None of the other authors has any conflicts of interest. | PMC10276024 | ||
References | PMC10276024 | |||
Background | Snacking is a common diet behaviour which accounts for a large proportion of daily energy intake, making it a key determinant of diet quality. However, the relationship between snacking frequency, quality and timing with cardiometabolic health remains unclear. | PMC10799113 | ||
Design | Demography, diet, health (fasting and postprandial cardiometabolic blood and anthropometrics markers) and stool metagenomics data were assessed in the UK PREDICT 1 cohort ( | PMC10799113 | ||
Results | Participants were aged (mean, SD) 46.1 ± 11.9 years, had a mean BMI of 25.6 ± 4.88 kg/m | PMC10799113 | ||
Conclusion | Snack quality and timing of consumption are simple diet features which may be targeted to improve diet quality, with potential health benefits. | PMC10799113 | ||
Clinical trial registry number and website | NCT03479866, | PMC10799113 | ||
Supplementary Information | The online version contains supplementary material available at 10.1007/s00394-023-03241-6. | PMC10799113 | ||
Keywords | PMC10799113 | |||
Introduction | Snacking can account for a large proportion of daily energy intake, making it a key determinant of diet quality [Snacks can be defined based on the time of day when consumed [The inconsistencies of snacking research render the impact of snacking on health unclear. Further investigation of snacking behaviour is warrante... | PMC10799113 | ||
Subjects and methods | SECONDARY | The This secondary analysis is a cross-sectional analysis of the baseline data and weighted logged diet data obtained as part of the original intervention trial. Out of the | PMC10799113 | |
Diet data | In the ZOE PREDICT 1 cohort, participants recorded all diet intakes during the entire study period on the specialised ZOE study app, yielding comprehensive records of timed intakes. Participants were trained to accurately record ad libitum diet intake using photographs, product barcodes, product-specific portion sizes ... | PMC10799113 | ||
Assessment of snacking | EVENTS | Snacks were defined as foods or drinks consumed between meals. Snacking events contained (1) a single food type, e.g. apple, or (2) multiple food types, e.g. apple, nut butter and coffee. For the single food snack type, drinks ≤ 50 kcal were excluded to ensure low-calorie drinks did not inflate snacking frequency. To d... | PMC10799113 | |
Diet quality scores | In order to capture the overall quality of the snacks an individual consumes, we created a snack diet index (SDI) in snackers (those consuming ≥ 1 snack/day). This used an adapted version of the plant-based diet index [ | PMC10799113 | ||
Hunger ratings | Participants reported their hunger levels on a visual analogue scale daily. App notifications appeared at | PMC10799113 | ||
Activity levels | Physical activity was self-reported, captured using the following question “In the past year, how frequently have you typically engaged in physical exercises that raise your heart rate | PMC10799113 | ||
Gut microbiome | Stool samples were collected by participants at home prior to the clinic visit using an EasySampler collection kit (ALPCO) and put into faecal collection tubes containing DNA/RNA Shield buffer (Zymo Research). A total of | PMC10799113 | ||
Cardiometabolic blood and anthropometric measures | The methods for anthropometric and biochemical measures are described in full elsewhere [ | PMC10799113 | ||
Statistical analysis | REGRESSION | Data analysis was performed using Python 3.8.3 edition (Pandas 1.3.3, statsmodel 0.13.2, scipy 1.7.1). Descriptive characteristics of the cohort and diet intakes were examined. The relationship between snacking frequency, quantity from energy and timing with the cardiometabolic health outcomes was assessed using analys... | PMC10799113 | |
Results | Participants were aged (mean, SD) 46.1 ± 11.9 years, had a mean BMI of 25.6 ± 4.88 kg/mCharacteristics of the cohortSnacking habits in the PREDICT 1 cohort ( | PMC10799113 | ||
Diet and snacking in the ZOE PREDICT 1 cohort | The average daily snack intakes in people who snack (95% of the cohort) were 2.28 snacks/day (95% CI 2.21–2.35) (Fig. The most popular foods consumed as snacks included drinks (milk, tea, coffee, fruit drinks), candy, cookies and brownies, nuts and seeds, fruits (apples, bananas, citrus fruits), crisps, bread, cheese a... | PMC10799113 | ||
Snacking quality versus habitual diet | Average snacking quality (SDI; lower scores are indicative of poorer snack quality) was 5.73 ± 2.09, range; 1–11, and IQR; 4–7 (Fig. | PMC10799113 | ||
Snacking frequency and energy quantity are not associated with cardiometabolic health | Across the snacking frequency groups (0, 1, 2 and > 2 snacks/day), there were no differences in cardiometabolic blood or anthropometric markers including anthropometric traits (height, weight, BMI, visceral fat, waist-to-hip ratio), or fasting and postprandial blood markers (see Supplementary Table 3) (all adjusted for... | PMC10799113 | ||
Snacking quality is associated with cardiometabolic health | TG | Participants consumed on average 74% of their snacking calories and 18% of their total daily calories from unhealthful foods. An inverse association was found between snacking frequency and quality (1 snack/d; 6.29 ± 1.67, 2 snack/day; 5.81 ± 2.11 and > 2 snacks/day; 5.23 ± 2.19, A sensitivity analysis was performed wh... | PMC10799113 | |
Minimally processed snacking is associated with cardiometabolic health | The SDI was inversely correlated with ultra-processed snacks (% of snacking energy from NOVA 4) (rho; -0.41, | PMC10799113 | ||
High-quality snacking versus low-quality snacking | Frequently snacking on high-quality foods (SDI Q1; ≥ 7, | PMC10799113 | ||
The relationship between timing of snacks and cardiometabolic health | Four clear temporal snacking patterns, capturing the timing and frequency of snack intake across the day, were evident (Fig. Patterns of snacking across the day. Furthermore, individuals who snack after 9 pm (32%), classified as late-evening snackers, had higher HbA1c concentrations (5.54 ± 0.42% vs 5.46 ± 0.28%, | PMC10799113 | ||
The relationship between snacking and the gut microbiome | The microbiome composition differentiated individuals based on their snacking quality (AUC = 0.617). As the frequency of snacking might also be related to the quality of the snack, we tested whether the gut microbiome was able to discriminate participants that snack rarely versus those that snack regularly, but we did ... | PMC10799113 | ||
Discussion | obesity, diet-related diseases | OBESITY | This research demonstrates snacking is a common dietary behaviour in a UK population accounting for 24% of daily energy intake. The relationship between snacking quality and main meal quality was low, highlighting the discordance between these two behaviours and their capturing of different dietary attributes suggestin... | PMC10799113 |
Supplementary Information | Below is the link to the electronic supplementary material.Supplementary Figure 1. CONSORT diagramSupplementary Figure 2. Correlations between diet quality indices calculated using meals and snacks. uPDI, unhealthful plant diet index; oPDI, original plant-based diet index; hPDI, healthful plant-based diet index; and SD... | PMC10799113 | ||
Abbreviations: | haemoglobinBody mass indexFood | MAY | Snack diet indexTriglyceridesIncremental area under the curveHomeostatic Model Assessment for Insulin ResistanceGlycated haemoglobinBody mass indexFood frequency questionnaireKate M. Bermingham, Anna May, and Sarah E. Berry have contributed equally to this work. | PMC10799113 |
Author contributions | SEB, AMV, JW, NS, PWF, TDS, GH and JC designed research. SEB, JW, GH and TDS conducted research. AM, KMB and FA performed statistical analysis. KMB, SEB, AMV, JW, TDS, NS, FA, ERL and LMD wrote the paper. SEB, TDS and KMB had primary responsibility for final content. All authors read and approved the final manuscript. | PMC10799113 | ||
Funding | Arthritis, Thomas’ NHS | ARTHRITIS, CHRONIC DISEASE | This work was supported by ZOE Ltd and TwinsUK which is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), ZOE Ltd, and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Researc... | PMC10799113 |
Declarations | PMC10799113 | |||
Conflict of interest | TDS, GH and JW are co-founders of ZOE Ltd. TDS, FA, NS, LMD, PWF, AMV and SEB are consultants to ZOE Ltd. TDS, SEB, JW, GH, LMD, PWF, AMV, JC and AM are in receipt of ZOE options. KMB, AM and JC are employed by ZOE Ltd. Other authors have no conflict of interest to declare. | PMC10799113 | ||
References | PMC10799113 | |||
Objective | SE, multiple sclerosis | MULTIPLE SCLEROSIS, CHRONIC INSOMNIA | Mindfulness is an established approach to reduce distress and stress reactivity by improving awareness and tolerability of thoughts and emotions. This study compares mindfulness training to sleep hygiene in persons with multiple sclerosis (PWMS) who report chronic insomnia, examining sleep efficiency (SE), self-reporte... | PMC10334613 |
Methods | SE, Insomnia | MULTIPLE SCLEROSIS | Fifty-three PWMS were randomized (1:1) in a single-blinded, parallel group design to ten, two-hour weekly sessions of Mindfulness Based Stress Intervention for Insomnia (MBSI-I) over a span of ten weeks or a single, one hour sleep hygiene (SH) session over one day. The primary outcome measure was SE, measured by the Fi... | PMC10334613 |
Results | SE | While neither SE nor the PSQI showed significant differences between MBSI-I, eMBSI-I and SH groups, ISI improved in both the MSBI-I and eMBSI-I vs SH at 10 weeks ( | PMC10334613 | |
Conclusion | insomnia | This pilot study demonstrates beneficial effects of MBSR on insomnia, sleep quality and quality of life in PWMS. | PMC10334613 | |
Trial registration | MAY | NCT03949296. 14 May 2019. | PMC10334613 | |
Keywords | PMC10334613 | |||
Introduction | anxiety, multiple sclerosis, Insomnia, SE, stress reduction | MULTIPLE SCLEROSIS, CHRONIC INSOMNIA | Twenty to fifty percent of persons with multiple sclerosis (PWMS) report having chronic insomnia (CI) [The clinical impact of chronic insomnia in PWMS, while frequently overlooked by clinicians, is supported by several studies demonstrating an overall lower quality of life [Insomnia is often treated pharmacologically w... | PMC10334613 |
Methods | PMC10334613 | |||
Study design | Insomnia | MAY, RECRUITMENT | This randomized parallel, single-blinded clinical study enrolled 53 participants with MS who were randomly assigned (1:1) to attend ten, two-hour weekly sessions of MBSI-I or a one-hour counseling session on SH. Repeated assessments were performed at baseline, 10 and 16 weeks. The evaluator was blinded to treatment gro... | PMC10334613 |
Treatment groups | PMC10334613 | |||
MBSI-I | In MBSR, participantsare taught under supervision to concentrate on the present moment intentionally and without judgment in order to reduce distress and emotional reactivity [ | PMC10334613 | ||
SH | This group attended a one-hour group counseling session based on a handout enumerating 15 sleep hygiene tips, published by the Centre for Clinical Intervention in Australia. The SH tips were as follows: (1) maintaining a consistent sleep pattern of going to bed and arising at about the same time each day; (2) attemptin... | PMC10334613 | ||
Recruitment procedures and participants | insomnia | Participants were recruited widely throughout the state of Connecticut via press releases distributed via paper and email to newspapers for articles and advertisements, MS support groups, neurologists, the Yale-Griffin Prevention Research Center electronic Newsflash, health magazines, and current and previous patients ... | PMC10334613 | |
Randomization and blinding | The former was carried out using SAS software for Windows version 9.4 (SAS Institute, Cary, NC) by dividing participants into blocks of 14, 17, and 22. The study coordinator enrolled the participants and assigned them to one of the two treatment groups based on the randomization algorithm. Therefore, the coordinator wa... | PMC10334613 | ||
Outcome measures | PMC10334613 | |||
Primary outcome | The study’s primary outcome was sleep quality defined by sleep efficiency, as measured by the Fitbit™ Charge 2 wrist device. This is a consumer wristband-tracking device that embeds a heart rate monitor and three-axis accelerometer to report heart rate, exercise and sleep. Raw data from the device was uploaded to Fitbi... | PMC10334613 | ||
Secondary outcomes | death, Muscle spasticity, insomnia, Insomnia, disability | SECONDARY, MULTIPLE SCLEROSIS | These included self-reported sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) at baseline, the end of the 10-week intervention, and 16-weeks post-intervention. The PSQI is a self-rated questionnaire to assess perceived sleep quality and disturbances over the prior one-month time interval [The Inso... | PMC10334613 |
Exploratory outcome measures | Within-group comparisons comparing baseline to 10 weeks and 16 weeks were done for the MBSI-I and SH cohorts. At the end of the randomized phase, participants in the sleep hygiene group were offered the same MBSI-I training and analyzed as a group, the expanded MBSI-I cohort (eMBSI-I). The eMBSI-I outcomes analyses inc... | PMC10334613 | ||
Adverse events reporting scheme | ADVERSE EVENT | Adverse events, including MS relapses, were recorded throughout the study by the coordinator. These were presented to the PI, who would inform the IRB as per the protocol. | PMC10334613 | |
Statistical analysis | REGRESSION | The sample size estimate allowed for 20% attrition and noncompliance to provide ≥ 80% power and maximum type I error of 5% to detect a minimal difference of 1.6 point improvement in subjective sleep quality as measured by the ISI sleep scale between cohorts. Generalized linear models were used to compare scores of the ... | PMC10334613 | |
Role of the funding sources | Neither Fitbit, Inc., which provided the Fitbit™ Charge 2 device as well as data tabulation free of charge, nor the funder of the study, had any role in study design, data collection, data analysis, data interpretation, or writing of the report. | PMC10334613 |
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